Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2

Related tags

Deep Learning CoaDTI
Overview

CoaDTI

Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2

Abstract

Environment

The test was conducted in the linux server with GTX2080Ti and the running environment is as follows:

  • python 3.7
  • pytorch 1.7.1
  • rdkit
  • pytorch geometric 1.6.3
  • Cuda 10.0.130

Data

Human dataset

C.elegans dataset

Binding_DB dataset

How to run

CoaDTI

  1. Run ./code/data_prepare.py to preprocess the dataset.
  2. Run ./code/train.py to train the CoaDTI.

CoaDTI-pro

  1. Run ./code/data_prepare.py to preprocess the dataset.
  2. Run ./code/train.py to train the CoaDTI-pro.
You might also like...
Graph Self-Attention Network for Learning Spatial-Temporal Interaction Representation in Autonomous Driving

GSAN Introduction Code for paper GSAN: Graph Self-Attention Network for Learning Spatial-Temporal Interaction Representation in Autonomous Driving, wh

Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

PyTorch Implementation of Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers 1 Using Colab Please notic

Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"

Memory Efficient Attention Pytorch Implementation of a memory efficient multi-head attention as proposed in the paper, Self-attention Does Not Need O(

We present a framework for training multi-modal deep learning models on unlabelled video data by forcing the network to learn invariances to transformations applied to both the audio and video streams.

Multi-Modal Self-Supervision using GDT and StiCa This is an official pytorch implementation of papers: Multi-modal Self-Supervision from Generalized D

Code of paper Interact, Embed, and EnlargE (IEEE): Boosting Modality-specific Representations for Multi-Modal Person Re-identification.

Interact, Embed, and EnlargE (IEEE): Boosting Modality-specific Representations for Multi-Modal Person Re-identification We provide the codes for repr

Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features

Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features | paper | Official PyTorch implementation for Mul

OOD Dataset Curator and Benchmark for AI-aided Drug Discovery

🔥 DrugOOD 🔥 : OOD Dataset Curator and Benchmark for AI Aided Drug Discovery This is the official implementation of the DrugOOD project, this is the

[CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
[CVPR'21] Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

TransFuser This repository contains the code for the CVPR 2021 paper Multi-Modal Fusion Transformer for End-to-End Autonomous Driving. If you find our

A Multi-modal Model Chinese Spell Checker Released on ACL2021.
A Multi-modal Model Chinese Spell Checker Released on ACL2021.

ReaLiSe ReaLiSe is a multi-modal Chinese spell checking model. This the office code for the paper Read, Listen, and See: Leveraging Multimodal Informa

Comments
  • Seems like lacking a script file(utils.py)

    Seems like lacking a script file(utils.py)

    When I ran 'python train.py', I got such error information: Traceback (most recent call last): File "train.py", line 22, in from utilts import * ModuleNotFoundError: No module named 'utilts' It seems that a script file named utils.py is lacking in code folder.

    opened by gsh150801 1
Owner
Layne_Huang
Bioinformatics, BioNLP
Layne_Huang
Analysis of Antarctica sequencing samples contaminated with SARS-CoV-2

Analysis of SARS-CoV-2 reads in sequencing of 2018-2019 Antarctica samples in PRJNA692319 The samples analyzed here are described in this preprint, wh

Jesse Bloom 4 Feb 9, 2022
SARS-Cov-2 Recombinant Finder for fasta sequences

Sc2rf - SARS-Cov-2 Recombinant Finder Pronounced: Scarf What's this? Sc2rf can search genome sequences of SARS-CoV-2 for potential recombinants - new

Lena Schimmel 41 Oct 3, 2022
The code for SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network.

SAG-DTA The code is the implementation for the paper 'SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network'. Requirements py

Shugang Zhang 7 Aug 2, 2022
This is the repo for the paper `SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization'. (published in Bioinformatics'21)

SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization This is the code for our paper ``SumGNN: Multi-typed Drug

Yue Yu 58 Dec 21, 2022
PyTorch code for the paper "Complementarity is the King: Multi-modal and Multi-grained Hierarchical Semantic Enhancement Network for Cross-modal Retrieval".

Complementarity is the King: Multi-modal and Multi-grained Hierarchical Semantic Enhancement Network for Cross-modal Retrieval (M2HSE) PyTorch code fo

Xinlei-Pei 6 Dec 23, 2022
This project aim to create multi-label classification annotation tool to boost annotation speed and make it more easier.

This project aim to create multi-label classification annotation tool to boost annotation speed and make it more easier.

null 4 Aug 2, 2022
Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal, multi-exposure and multi-focus image fusion.

U2Fusion Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal (VIS-IR, medical), multi

Han Xu 129 Dec 11, 2022
Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral)

DSA^2 F: Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral) This repo is the official imp

如今我已剑指天涯 46 Dec 21, 2022
Code and pre-trained models for MultiMAE: Multi-modal Multi-task Masked Autoencoders

MultiMAE: Multi-modal Multi-task Masked Autoencoders Roman Bachmann*, David Mizrahi*, Andrei Atanov, Amir Zamir Website | arXiv | BibTeX Official PyTo

Visual Intelligence & Learning Lab, Swiss Federal Institute of Technology (EPFL) 385 Jan 6, 2023
AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation

AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation A pytorch-version implementation codes of paper:

null 11 Dec 13, 2022